# Multi‐contrast X‐ray microtomography of human lung specimens with an extended field‐of‐view

## TL;DR

This paper introduces a new X-ray imaging method that doubles the field-of-view while maintaining high resolution, enabling detailed imaging of large human lung samples.

## Contribution

A novel multi-contrast and multi-scale X-ray microtomography approach with an offset geometry to extend the field-of-view.

## Key findings

- The method doubles the achievable field-of-view for a given spatial resolution.
- Multi-contrast reconstructions of human lung tissue were demonstrated at 10.5 μm and 450 nm voxel sizes.
- The technique is compatible with various X-ray phase-contrast imaging systems.

## Abstract

Phase‐based X‐ray microtomography is a powerful technique capable of quantitative volumetric imaging of lung tissue in health and disease. The maximum sample size is however limited by the fixed sizes of detectors and optical elements. Thus while high‐resolution imaging can offer valuable microscale insights, it can be difficult to interpret without the context of the surrounding tissue. We propose a multi‐contrast and multi‐scale approach, combined with an offset geometry to extend the field‐of‐view (FOV).

FOV limitations make it a challenge to simultaneously achieve high spatial‐resolution and image large samples. Our method doubles the possible FOV achievable for a given spatial‐resolution, in a way compatible with multiple scales and imaging systems.

Multi‐contrast whole sample volumetric images are acquired using a beam‐tracking X‐ray phase‐contrast imaging(XPCI) system. Following this, a section of the same sample is imaged at higher resolution using an X‐ray microscope with propagation‐based imaging. The FOV of both methods is doubled using an offset center‐of‐rotation geometry, followed by weighted analytical reconstruction.

We present exemplary multi‐contrast reconstructions of resected human lung tissue at 10.5 μm voxel size across a 4.3 cm horizontal FOV, and at 450 nm voxel size for a 2.7 mm section of the same sample. This enables the visualization of a range of features, from the macro to the cellular scale.

We demonstrate a versatile method to image large samples without sacrificing spatial‐resolution. This method is directly compatible with complementary implementations of XPCI, and is easily adapted to a range of other systems.